DeustoTeka
DeustoTeka recoge la producción científica del personal docente e investigador de la Universidad de Deusto. Su propósito es reunir, archivar, preservar y aumentar la visibilidad en acceso abierto de los resultados de investigación.
Comunidades en DSpace
Selecciona una comunidad para explorar sus colecciones.
Envíos recientes
Using LinkedIn endorsements to reinforce an ontology and machine learning‐based recommender system to improve professional skills
(MDPI, 2022-04-08) Urdaneta Ponte, Maria Cora; Oleagordia Ruiz, Ibon; Méndez Zorrilla, Amaia
Nowadays, social networks have become highly relevant in the professional field, in terms of the possibility of sharing profiles, skills and jobs. LinkedIn has become the social network par excellence, owing to its content in professional and training information and where there are also endorsements, which are validations of the skills of users that can be taken into account in the recruitment process, as well as in the recommender system. In order to determine how endorsements influence Lifelong Learning course recommendations for professional skills development and enhancement, a new version of our Lifelong Learning course recommendation system is proposed. The recommender system is based on ontology, which allows modelling the data of knowledge areas and job performance sectors to represent professional skills of users obtained from social networks. Machine learning techniques are applied to group entities in the ontology and make predictions of new data. The recommender system has a semantic core, content‐based filtering, and heuristics to perform the formative suggestion. In order to validate the data model and test the recommender system, information was obtained from web‐based lifelong learning courses and information was collected from LinkedIn professional profiles, incorporating the skills endorsements into the user profile. All possible settings of the system were tested. The best result was obtained in the setting based on the spatial clustering algorithm based on the density of noisy applications. An accuracy of 94% and 80% recall was obtained.
Identifying the skills requirements related to industrial symbiosis and energy efficiency for the European process industry
(Springer, 2023-07-20) Akyazi, Tugçe
; Goti Elordi, Aitor; Báyon, Félix
; Kohlgrüber, Michael; Schröder, Antonius Johannes
The need for sustainable production, efficient use of resources, energy efficiency and reduction in CO2 emission are currently the main drivers that are transforming the European process industry besides Industry 4.0. Since the potential of industrial symbiosis (IS) and energy efficiency (EE) about environmental, economic and social issues has been discovered, the interest in them is gradually increasing. The funding and investments for IS and EE are highly encouraged by the European Commission, while more and more policies as well as research and innovation (R&I) activities are initiated to promote European industry’s advancement towards a circular economy and CO2 neutrality. The aim is to maintain the competitiveness and economic progress of the industry. The key to build a competitive and sustainable European manufacturing industry is to create a competent, highly qualified workforce that is capable of handling the new business models coming with IS and EE requirements and digital technologies. We can generate this by identifying the skills needs and upskilling and reskilling the current workforce accordingly by delivering the suitable training programmes. Therefore, this work identifies the most critical skills needs related to IS and EE for six different energy-intensive sectors (steel, ceramic, water, cement, chemical and minerals) in Europe. The effect of the digital transformation on the skills needs is as well discussed. The identified skills are aimed to be included in vocational education and training (VET), tertiary education and other kinds of training curricula. We also identify the cross-sectoral most representative job profiles linked with EE and IS in these sectors and demonstrate the methodology for the selection process. Furthermore, we present a key tool for identifying the most significant current and future skills requirements. Also, we define the critical skill gaps of the European process industry using this tool. Once the skill gaps are defined, they can be reduced by delivering well-developed continuous trainings. We also link our work to the respectable ESCO, the European Classification of skills, competences, qualifications and occupations, to attain a common ground with other studies and frameworks, minimise the complexity and contribute to their work. Our work is developed to be an academic and industrial guideline to prepare well-developed training programmes to deliver the needed skills.
Building policy capacities for tackling grand social challenges exploring the boundary-spanning potential of university research in the social sciences
(Gobierno Vasco = Eusko Jaurlaritza, Departamento de Economía, Trabajo y Empleo = Ekonomia, Lan eta Enplegu Saila, 2023-02) Arrona Etxaniz, Ainhoa; Magro Montero, Edurne; Wilson, James Ralph
The emergence of new approaches to regional policy, including the well-known concept of smart specialisation strategies and the need to face grand societal challenges have highlighted the importance of policy capacities among public and private stakeholders. These challenges have increased the pressure on universities and their academic staff to assume more engaged roles within their respective territories. This article explores how social sciences research can contribute to regional policy capacities for tackling social challenges. Specifically, it focuses on the institutional arrangements that universities develop to facilitate engaged research in regions, or what have been labelled «university-based boundary organisations». We suggest that they are a relevant regional instrument due to their integration of knowledge bridging and knowledge coproduction functions. The paper explores how these roles contribute to regional policy capacities through analysis of the case of Orkestra-Basque Institute of Competitiveness, a university-based boundary organisation in the Basque Country.
Application of the k-prototype clustering approach for the definition of Geostatistical estimation domains
(MDPI, 2023-02-01) Hernández, Heber; Alberdi Celaya, Elisabete; Goti Elordi, Aitor
; Oyarbide Zubillaga, Aitor
The definition of geostatistical domains is a stage in the estimation of mineral resources, in which a sample resulting from a mining exploration process is divided into zones that show homogeneity or minimal variation in the main element of interest or mineral grade, having geological and spatial meaning. Its importance lies in the fact that the quality of the estimation techniques, and therefore, the correct quantification of the mineral resource, will improve in geostatistically stationary areas. The present study seeks to define geostatistical domains of estimation for a mineral grade, using a non-traditional approach based on the k-prototype clustering algorithm. This algorithm is based on the k-means paradigm of unsupervised machine learning, but it is exempt from the one-time restriction on numeric data. The latter is especially convenient, as it allows the incorporation of categorical variables such as geological attributes in the grouping. The case study corresponds to a hydrothermal gold deposit of high sulfidation, located in the southern zone of Peru, where estimation domains are defined from a historical record of data recovered from 131 diamond drill holes and 37 trenches. The characteristics directly involved were the gold grade (Au), silver grade (Ag), type of hydrothermal alteration, and type of mineralization. The results obtained showed that clustering with k-prototypes is an efficient approach and can be used as an alternative or complement to the traditional methodology.
Abuelas defendiendo a sus nietas: un caso de asesinato en el San Sebastián del siglo XVII
(Universidad de Viena, 2023-07-30) Intxaustegi Jauregi, Nere Jone